{
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  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "(768, 9)\n"
    }
   ],
   "source": [
    "# 第五周作业\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# 第一步数据探索\n",
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\ncount   768.000000  768.000000     768.000000     768.000000  768.000000   \nmean      3.845052  120.894531      69.105469      20.536458   79.799479   \nstd       3.369578   31.972618      19.355807      15.952218  115.244002   \nmin       0.000000    0.000000       0.000000       0.000000    0.000000   \n25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n75%       6.000000  140.250000      80.000000      32.000000  127.250000   \nmax      17.000000  199.000000     122.000000      99.000000  846.000000   \n\n              BMI  DiabetesPedigreeFunction         Age     Outcome  \ncount  768.000000                768.000000  768.000000  768.000000  \nmean    31.992578                  0.471876   33.240885    0.348958  \nstd      7.884160                  0.331329   11.760232    0.476951  \nmin      0.000000                  0.078000   21.000000    0.000000  \n25%     27.300000                  0.243750   24.000000    0.000000  \n50%     32.000000                  0.372500   29.000000    0.000000  \n75%     36.600000                  0.626250   41.000000    1.000000  \nmax     67.100000                  2.420000   81.000000    1.000000  \n"
    }
   ],
   "source": [
    "print(train.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据中存在缺失值，口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度、舒张压、三头肌皮褶厚度、餐后血清胰岛素、体重指数（体重（公斤）/ 身高（米）^2）不会存在0得情况，0就是缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Glucose            5\nBloodPressure     35\nSkinThickness    227\nInsulin          374\nBMI               11\ndtype: int64\n"
    }
   ],
   "source": [
    "# 存在数据缺失得字段\n",
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glucose、BloodPressure和BMI 数据缺失较少，SkinThickness和Insulin数据缺失较多，可以对数据进行补充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count of Outcome')"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标（target：是否得糖尿病）\n",
    "sns.countplot(train['Outcome'])\n",
    "plt.xlabel('If Outcome')\n",
    "plt.ylabel('Count of Outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到发病得人数比不发病得要少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数直方图\n",
    "sns.countplot(train['Pregnancies'])\n",
    "plt.xlabel('Number of Pregnancies')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到大部分人怀孕次数在0-4次，怀孕一次的最多，其次是没怀孕过的，10次以上的很少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a19cef0d0>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看怀孕次数和得病是否有关系\n",
    "sns.countplot(x=\"Pregnancies\", hue=\"Outcome\", data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从直方图可以直观的看出，怀孕次数越趋高，得病率就越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 血浆葡萄糖浓度\n",
    "fig = plt.figure()\n",
    "sns.distplot(train['Glucose'], kde = False)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "人的血浆葡萄糖浓度50以上，集中在75-150之间，0是缺失的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Glucose', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('If Outcome', fontsize=12)\n",
    "plt.ylabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "健康人群的血浆葡萄糖浓度比较平稳，得病人群的普遍较高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 舒展压\n",
    "fig = plt.figure()\n",
    "sns.distplot(train['BloodPressure'], kde = False)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正常情况舒张压不会为0，低血压和高血压属于中少数人群，大部分人的血压稳定在60-80之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'BloodPresure')"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zs6eFIwyeUOuQbhMKCwuZMeNlvAmd8SQ2HGlVqzp1ILjjmfrcc2FxD1OpUPb555/zyiuvcHpqFRd3bf7tpCO5XfD7UwqprihhwvjxlJWVtWCUwaPJIciMMTz33HOUlZdT2W14462GWk4XFemnkbt9O7Nnzw5ekEq1MUuXLmXSo4/Qp52XmzNLj/ln2RxdYn2MGVjM9u3fc//94w+b1NpaaHIIsn/9618sXLiQqi5D8EU3PYfBk9SdmuTezJw5k/Xr1wchQqXaljVr1vCHiRPo5K7hrqwiIp0t87qnJNfw2wElrF61mocefLDV1UvT5BBE27dv55kpU/DFp1LdeVCzn1fZfTi+iBgeeuhhioqKLIxQqbZlxYoV3HP33SS6qrg3u5C4iJYttzM8tZob+pWy5OuvmThxAlVVR59pHWo0OQRJeXk5E//wB6p9Qnmv8w6b19AkVxRlvc5nb0EBj06apCvFKdUCli5dyrj77iU5opIJgw+SGGVNHbbz06q4KbOUpd8sZfy4cZSXl1tynZamySEIPB4PDz38MHl5eZT1PA8TGXvcr+GLS6Gy6w9Y+s03vPjiCdcjVEoBCxYsYPy4caRGVVmaGGqd16WK0QNKWLlyBXf+/g4KCwstvV5L0ORgMWMM06dP55uvv6ay2+l4Ezqf8GvVdMykOnUA7733Hv/85z9bMEql2o45c+bw6KOP0Du+ivsHFzZ7BvTJOrNTNXcMKmbb1s2Mue1/2L07tOf3anKw2FtvvcX//u//Ut3pFGo6nny1xqqup+FJ7Mbz06axcOHCkw9QqTbC5/Pxl7/8hWnTpjGkfTX3Zhc1WBPaakM61DAuu4iD+3bxP7f+jk2bNgX1+sdDk4OFPvjgA1599VVq2vemKv3UlnlRcVDR6zx8cSk8OmkSS5eG7TxBpVpMdXU1kyZN4p133mFEWiVjB5W02Kik49U30cMfhhTiqCpi7O23s2TJEnsCaYImB4t88sknPDt1Kp526VT2OPvY8xmOl9NFWZ8L8UQmMGHCRFatWtVyr61UmCkpKeG+e+9lwYIFXN2rjOv6luFowT/HE5EW6+XBoQdIjazg/vvv58MPP7Q3oEZocrDA/PnzeeKJJ/DGd6aizwXgsODH7IqirO+PqHZGc+9997F27dqWv4ZSrdzevXu5fcxtrF69kt8NKOHSHpUt+jntZCRFGSYMOcjAxCqeeuopXn/99ZBauVKTQwv79NNPmTRpEt64VMr7jACHdespmYhoSvuOpEqiuOvuu7UFoVQ927Zt439u/R35u3K5N6uIMzqF3iS0aBfcmVXM2Z0qmTlzJs8880zIlMrR5NCCPvzwQyY//jie+E6UZVwIzgjLr2kiYyjtezFV4uaee+9l+fLlll9TqVC3evVqxt4+Bk/pASYOKWRAcmi84TbG5YBb+pdxWfdy5s2bx0MPPRgSk+U0ObSQd999l6eeegpvQhfK+wQnMdQykTGU9htJlTOG++4bpyvIqTZtyZIl3HP3XcRRxoNDD9ItLvQnjYrAVb0ruK5vKYv+s4jx4+6zfbKcJoeTZIzhlVde4YUXXqAmqQflfX4IzuAvzW0iYijtdzHV7kQefPBBPvroo6DHoJTdvvzySyZOmECnqCr+MOQgHaJ9dod0XH6YXsVvB5SwauVK7rzz95SUlNgWiyaHk+DxeHjiiSd46623qE7pS2Xv88Bh0/g4AJebsr4jqYnrxJNPPsmsWbNCqoNLKSt9/vnnPPzwQ/SIq+b+IcGb3NbSzuhUzdhBxWzZvIl77r7LtgShyeEElZaWMm78eD755BOqugyhqvuZx1cvySrOCMozLqSmfW9ee+21kOrgUsoqixYt4pFH/kivuBruzS4ixmVNYnhrUwzbS5xsL3EyeXkCb22KseQ6QzrUMPaUYrZu2cy999xtyy2mEHg3a33y8/O5bcwYli1bTkWPs6hOG9Ky8xhOlsNJZc9zqOqcxbx58xg3fjylpaV2R6WUJVatWsXDDz1Et9ga7skuItqixACQW+qiwuugwutgQ2EEuaXW3UIe0qGGMQOL2bRpEw8+8Iegl/y2NTmIyJ0islZE1ojI2yLiFpGeIrJERDaLyN9EJNLOGI+0ceNGfvu735Gbt4vyjAvxpPS1O6TGiVCdnkNlj7NYtmw5t40ZQ35+vt1RKdWi8vLymHD/eNpHVnN3lrWJwQ5DU2q4ObOUpcuW8/TTTwf1NnGzkoOIxIjIAyLycuBxhoiMOpkLi0gaMBbIMcacAjiBXwBPAlONMRnAQeDmk7lOS/rqq6+4/faxFFZ4KM38Md52aXaH1KSalL6UZ1xIbt4ufvvb37Fx40a7Q1KqRZSVlTHx/vFQU849Wa23j6EpZ3eu4sqe5Xz22We89957Qbtuc1sOrwNVwOmBx3nApBa4vguIFhEXEAPsBi4Aan8CM4ErWuA6J23OnDn84YEHqIxMoDRzVLNWcQsV3nZplGb+mMJKD7ffPpZFixbZHZJSJ23KlCnsyMtjzMAiUlrZqKTjdVmPCoalVPPiCy8ErRpCc5NDb2PMU0ANgDGmAjipm+zGmJ3AM0Au/qRQBCwDCo0xtT2oeUCjH89FZLSILBWRpfv27TuZUI6pfiVHT2JXyvpejImItux6VvFFJ1GaOYrKiAQmTpzI3Llz7Q5JqRO2cOFCFixYwBU9yhmQFP4DLhwCo/uXkuQ2TH5sEhUVFdZfs5nnVYtINGAARKQ3/pbECRORJOByoCfQBYgFLm7k1EbbisaYGcaYHGNMTkpKysmEclRer5cnn3ySd955h+qOmVT0vsCSOQxRuYtxlu/HWb6f6A0fEpW7uMWvAf5yG2X9RuJJSOOZZ57hrbfesuQ6SlmpvLyc56Y+S88EL5d2t/5NMlREuwy/ySxi567dzJ492/LrNTc5PAR8DHQVkdnAfOC+k7z2D4HvjDH7jDE1wBzgDCAxcJsJIB3YdZLXOSEej4fHHnvs0FDVbqdbNlTVUX4A8dYg3hpcJXtwlB+w5DqAf6hrnx9Sk9ybV155JeSKfSnVlHfffZfComKuyyjF2cbGWw5I8jC8YxV///u77N+/39JrNfmjFREBNgBXAjcAb+PvRF54ktfOBYYHOrsFGAGsA/4NXBU453rg/ZO8znHz+Xw8/vjjLFiwgKr0nNAbqnqyHA4qe51NTYcMZs6cyZtvvml3REo1S1VVFX9/928MS6mid7vwv53UmJ/2Kqemupo5c+ZYep0mk4Pxf6z8X2PMfmPMPGPMXGNMwcle2BizBH/H83JgdSCWGcA44C4R2QK0B1492Wsdr5dffpn58+dTlTaM6s5Zwb58cIiDyh5n1U2W+/jjj+2OSKkmLVq0iLLyCkak2V+Yzi6pMT5OSa7m008+xuezriO+uY2yxSLSQkuZHWKMecgYk2mMOcUYc60xpsoYs80Yc5oxpo8x5mpjTFB/Cz799FPefvttqlMywzcx1BKhssdZeBO68NTTT+uaECrkLVq0iHZRMCCpxu5QbHV6ahX7CvazefNmy67R3ORwPvBfEdkqIqtEZLWIhN3iAXv27GHq1OfwxadS1X14eN1KOhqHk/Le5+NzRTPpsceCMgpCqRO1dfMmesRV276Sm916xftvqW3bts2yazQ3OVwM9MY/B+FSYFTga1h57rnnqKzxUN7znNCokxQsrijKe5zN7l27mTVrlt3RKHVUe/Lz6RhtXwnuCo/gdru56qqrcLvdVHjsyVIdA/M6rKx60Nx3QHOUf2Fj48aNLF68mMrUQZioeLvDCTpvQmdqknsxZ84/KSwstDscpRrlcrnwGfuaDeUeYdSoUYwZM4Yf//jHlNuUHGrffF0u62o7NfeV5+GPRwA3/rkJG4GBFsUVdO+99x7iiqI6tb/dodimuks2VWu2Mm/ePK655hq7w1GqgYSEBA5WWTjUuwkxLsPcuXMxxjBv3jxSbarldLDK/7k+Pt66D7LNajkYYwYZY7ICXzOA04CwWW7M5/OxeMnXVLdLB2dI1fkLKl90Iia2A4uXLLE7FKUaNSgrm03FUfhsum8R7TJUVlbyj3/8g8rKStsK/W0s9H+uHzRokGXXOKEb68aY5UCLj16yy44dOygpLsKT0MXuUGxXE9+ZtWvX6hoQKiTl5ORQWm1YfSB4y/CGov/ku2mfnESPHj0su0azbiuJyF31HjqAYYB1BY2C7ODBgwCYyFh7AvBW43a7GTVqFHPnzqXUG9y67fX5ImPxeb2UlpaSmJhoWxxKNeacc86hQ/tk5m33kJVc1CYGFB7p+xInaw9EMHr01Tgc1g2cae4rx9f7FwXMxV8XKSzUDt80juCv/QwgnurDOrnEY19yqP0Z6JBWFYoiIiL41TW/ZkOhi//mt71bwF4fvLExnoT4OC691NoBo816NzTG/LF2W0QcQJwxptKyqIKsffv2ADhqKrCj8K9xRR7WyWVc1iw92ByOGn9SSEpqPSXJVdty+eWX86/PPuWtLRvITDxIsju8y3XX92Gum23FTh544E5LO6Oh+Yv9/FVEEkQkFn/9o40icq+lkQVR586dAXBUHLQnAGfkYZ1cdnaKOyoKaZeYhNvtti0GpY7F6XQy/v4JeB1RTF3djso20j32bUEE722L5bzzzuOCCy6w/HrNva00wBhTjH/hnQ+BbsC1lkUVZPHx8fTu3QdXsS0FYEOHMUSW7uLUnGF2R6LUMXXr1o2HHv4jO0qdvLA2Hk+YNx62Frt4YV07+vbNYPz48UgQOluamxwiRCQCf3J4P1BiO6wmwZ155hk4S/ORqhK7Q7GNs3gXprqC008/vemTlbLZ8OHDueP3v2fF/kimrwnfBLG1yMVTK9uR3D6FyY8/EbRWfXOTw0vA9/gX5PlCRLoDxVYFZYdRo0bhEAeR+evsDsU2kflrSUxM4uyzz7Y7FKWa5fLLL2fs2LEsL4jk+dXxVNlXWcMSGw76E0Ni+1Se/9P0uv7RYGjuJLhpxpg0Y8wlxm87/mJ8YaNjx46MGHEBUQUbkapSu8MJOmfxblxFeVx11U+JjGx7o0BU63XllVdy1113sfpAFI9/m0hxdXiMb12cH8lTK9uR0jmd56f9iY4dOwb1+s3tkE4VkVdF5KPA4wH4F+IJK7fccgsRTifuHYuhLa2O5vMRvWMxHTumctVVVzV9vlIh5rLLLuORRx8lr8LNI8uT2FXWegtnGgNzt7t5YW08/QcMZPqfXwh6YoDm31Z6A/gE/1rPAJuA31sRkJ1SU1O58cYbcB3MJaLAujrpoSYq7xuk/CC///0dOkpJtVpnnXUWU597juqIdvxxeRIr97f8LOpucR6inT6inT4yE2voFteyQ6WqvfDSujje3RrL+eefzzNTniUhIaFFr9FczU0OHYwx74J/GoAxxgOE2d09v5/97GcMHTqU6Nz/4ig76QXvQp7rwHdE5q/lJz/5CWeccYbd4Sh1UgYOHMhLM14mrVsvnl2VwLzt7ha9CfDrvuV0j/fSPd7LhKHF/LpveYu99oEqB5O/TWRRfhQ333wzDz74IFFRUS32+serucmhTETaExihJCLDgSLLorKR0+nkwQcfJDk5mbgt/0Iqw6rf/TDOkj3EfPcl/TIzufXWW+0OR6kWkZqayp+m/5lzzjmXv22N5cW1cSHfUb2x0MVDS5PYXR3Do48+yrXXXhuU4arH0tzkcBfwAdBbRP4DzAJuP9mLi0iiiLwnIhtEZL2InC4iySLymYhsDnwN+lTdxMREnp3yDLFRTuI2fxKWHdSOsgJit/yLtLTOPPnEE9oJrcJKdHQ0Dz/8MKNHj2bJvigeWZ7EvorQ7IdYsDOKJ75tR1z7zrz4l5dCZrRgkz+tQLkMN3AucAbwW2CgMaYllgl9HvjYGJMJZAPrgfHA/EBp8PmBx0HXvXt3nnn6aaLFS9zGD3FUhM8COM7iXcRt/IiU5CSenTJFC+ypsCQi/OpXv+LJJ5/ioC+Oh5YlsfaAPfXTGlPjg9c3xPLGxjhyTj2Nv7w0w9Iqq8eryeRgjPEBU4wxHmPMWmPMmsAkuJMiIgnAOcCrgetUG2MK8Rf0mxk4bSb+iXe2yMzMZNq052kX7fIniNK9llzHF5OMcUZgnBF44jvhi0m25DoArgPbiNn8Gd26pn2GVUgAABpsSURBVPHCC3+2ZRSEUsF02mn+N972nbry9Mp2fJZn/6CL4mrhyRXt+PcuN9dccw2TH3/c8lpJx6u57axPReSn0rI3wXrhL/v9uoh8KyKvBGo3pRpjdgMEvjb67iUio0VkqYgs3bfPuurhGRkZvPDnP5PaIYnYjR/hsmAUU1W34Xhj2uONaU9F5iVUdRve4tfAGCJ3Lid660IG9M/kT9Om0aFDh5a/jlIhKD09nRf/8hLDh5/Om5timbkxFq9NM6rzSp38cVkS35dF8+CDD/Kb3/wGp9NpTzDHcDx9Dn8HqkSkWERKRORke2pdwFDgRWPMEKCM47iFZIyZYYzJMcbkpKSknGQox5aWlsaMl15iSHY20d99SVTuEvC1orn6nmqit84natcKRo4cyXNTp9o2PE4pu8TExPDopEn8/Oc/Z/5ON1NXJwS9o3rdQRePfpuI153E89OmBaWA3olq7gzpeGOMwxgTaYxJCDw+2XeXPCDPGFO7JuV7+JNFvoh0Bgh8teZeznFKSEjg6aef4sorryQyfy2xmz5CqsvsDqtJjvIDxG/4gMiiPG677TbGjRunnc+qzXI6ndx6663cfffdrDkQGdQZ1UvyI3lmZTs6du7KX16aQf/+ob1e/TGTg4h0FJHnRGSuiEwO9BO0CGPMHmCHiPQL7BqBvxz4BxyafX098H5LXfNkuVwuxo4dywMPPEB0dRHx6z/AWbTT7rCOyrVvE3Eb5pLodvHcc89x9dVX2z48TqlQcOmll/LHRx4hr8LN5G+TKKyy9u/iy91RvLA2nsz+A/nT9NbR19dUy2EW/ts9f8K/Cty0Fr7+7cBsEVkFDAYmA08AF4rIZuDCwOOQMmLECGbMeIn0TinEbPqEyJ3LwYTQbSZvDe5tXxD9/VdkDzqF1159haysLLujUiqknH322Tz19NPs90TxxArrEsTnu6J4ZX0cw4YN5ZkpU1rNLd2mkkMnY8xEY8wnxpjbgRZ9hzHGrAj0G2QZY64wxhw0xuw3xowwxmQEvh5oyWu2lO7duzPjpZe46KKLiNq1gphNnyA19i+t6agoJG7DXCIObOX6669nypQpJCdbN/pJqdZs8ODBPPXU0xzwRPHUykTKalo2QSzJj+S1DXHknJrDY5Mfb1XlaZpKDiIiSYGJacmA84jHbVp0dDQTJkxg3LhxuCsKiFv3Ps6SfNvice3fRtz6/yPB5eOZp5/mxhtvDMlREEqFkuzsbB6b/Dh7KiJ4fnUC1S3USb3uoIuX1sdzyikDmTTpMVtLYZyIppJDO2BZvX8JwPLA9lJrQ2s9Lr74Yl588UU6t08kZuNHROSvC25VV+MjKncJ0dsW0r9fX1579RVycnKCd32lWrlhw4Zx/4QJbCh08eam2JN+vX0VDv60ph1p6V2Z/PgTrS4xQBPJwRjTwxjTyxjTs5F/vYIVZGvQp08fXn55BqefPhx37mKiti8CXxDGyXmqiNn8WV3xvGnTnsfqob1KhaMRI0Zw3XXX8fluN//eeeJv5tVemLamHRIZw+THnwi5yW3Ndcy55CIy9FjHjTHLWzac1i0uLo7HJk3i1VdfZfbs2TiriinvPQJc1gwdlapS4jZ/irO6hDvvuYdRo0ZZch2l2orrr7+edevW8ta3y8hMrKFz7PEPNPn7thi2lzh4/PEHSEtLsyDK4GjqttKUwL8/A0uAGcDLge2WHrkUFhwOB7/5zW+YMGECEaV7idv0EVLTcmV9665TfpC4jfOIcdQwZcoUTQxKtQCn08n990/AHR3LyxsS8B3n3eGNhS4+3RHNFVdc0erXYm/qttL5xpjzge3A0MDIomHAEGBLMAJsrS666CIef3wyUTWlxG34sEUnzDnKCojb9CFJsVFM/9OfGDx4cIu9tlJtXfv27bnj93eypcjJwl3Nv73k8cEbm+LplNqR3/72txZGGBzNLZ+RaYxZXfvAGLMG/7wEdQw/+MEPmDr1WdxSQ1wLDXV1lB8gbvOntE9M4IU//5levbTrR6mWNmLECLKzsnjvu7hmD29dsNPNzlIHt90+lujoaIsjtF5zk8P6QGG880TkXBF5GX95bdWEgQMH8tSTTxLprSB20yfgPfGCtrV9DEkJsTz/3HN07ty5BSNVStUSEcbcfjul1fDxjqbnJlR54YPcWIYMHsyZZ54ZhAit19zkcCOwFrgD/9rR6wL7VDNkZWXx2GOTcFYWEr1t4YnNpvZWE7vlX7hdMPXZZ1t1R5dSrUFGRgbnnHMOn+2MabL18O+dboqr4Kabbw6bEjXNLbxXib9T+kHgAWB6YJ9qplNPPZWxY8fiKtxB5M5vj+/JxhD93Zc4Kwt59JFH6N69uzVBKqUOc+2111JeA1/sPnrfg9cHn+yMJTs7i0GDBgUxOms1KzmIyHnAZmA68AKwSUTOsTCusHTFFVcwcuRIonavxFm8u9nPi9i3EdfB7YwePVontykVRBkZGWQNOoV/7YqpG7nULc5DtzhP3Tkr9kewvwKuuupqm6K0RnNvK00BLjLGnGuMOQf4ETDVurDC19ixY+mSlkbM9182q/9BKouJzvuaYTk5/OxnPwtChEqp+i6/4ifsKxc2FPqnhf26bzm/7ntoePoXu910aJ/c6oeuHqm5ySHCGLOx9oExZhMQYU1I4S0mJoaJEyZAVSlRTd1eMgZ37mIiI1yMu+8+HI7QXCBdqXB25plnEhPt5j97Gt5aKqkWVu2P5MKLfoTLFTrrU7eE5r7bLBWRVwOjlc4LjFZaZmVg4WzgwIH8+Mc/JnLvWqSi6KjnOYt24CrK45abb24V9d+VCkdut5uzzj6H5QVuPEeMJVleEInXENIrup2o5iaHW/GPVhqLf8TSOuB3VgXVFtxyyy1ERUYRtesoFUiMIXrncjp36cJPfvKT4AanlDrMWWedRVkNbCo6vHWwvCCS1I4p9OnTx6bIrNPc0UpV+DujH+LQaKUqKwMLd0lJSVx99VVEHPgOR0Vhg+Ouwlyk/AA33Xhj2DVXlWptcnJycDodrN5/qE6axwfrCqMYfvoZYTN8tT4drWSjn/70pzhdLiL2rmtwLHLvOjqkpHD++efbEJlSqr6YmBgG9B/AusJDyWFrsYsqj2HYsGE2RmYdHa1ko6SkJH44YgRR+7cetv6Do6IQZ/FufnrlldpqUCpEZA8ezPYSJ1WBSvybA7eYwnUJXttHK4mIU0S+FZG5gcc9RWSJiGwWkb+JiDX1rkPEyJEjMd4axHNoTqHrwDZEhIsuusjGyJRS9Q0YMACfge9L/Elha1EEaZ07kZiYaHNk1giF0Up3cHidpieBqcaYDOAgcHMLXSckZWdnk5ScfFhyiCrcTnZ2Nu3bt7cxMqVUff369QNgeyA55JZH0jezv50hWcrW0Uoikg78GHgl8FiAC4D3AqfMBK442euEMofDwenDh+PwVoMxSFUplB/kjDPOsDs0pVQ9ycnJtIuPY0epkwqPsK+csK6K3OzRSsaYZ40xVxpjfmKMmdpCo5WeA+4DakcPtwcKjTG1c9PzgEYrzInIaBFZKiJL9+3b1wKh2GfYsGH+xODz4CzZc2ifUipkiAjduvdgT4WLPeX+t85wrnPW1DKhq4GjroVkjDnhnhgRGQXsNcYsC4yGAmhsPFij1zfGzMC/Mh05OTnHuV5TaBkwYAAA4q3BWbaPqCg3PXr0sDcopVQDaenpLNmyhvwKp/9xGFdHbmoojJVrT54JXCYilwBuIAF/SyJRRFyB1kM6sMvCGEJCp06dcDid+Lw1OMsPkJGRgdPptDsspdQRUlNTKayEvYHk0KlTJ5sjsk5Ty4RuP/IfUAbkBrZPmDHmfmNMujGmB/ALYIEx5hrg38BVgdOuB94/meu0BiJCYrt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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 舒张压和得病的关系\n",
    "sns.violinplot(x='Outcome', y='BloodPressure', data=train, hue='Outcome')\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('BloodPresure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无法很好的从舒张压看出和得病有所关系，无论是否得病可见的舒张压范围差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 三头肌皮褶厚度\n",
    "sns.distplot(train['SkinThickness'], kde = False)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三头肌皮褶厚度符合正态分布，0为缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'SkinThickness')"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 三头肌皮褶厚度和得病的关系\n",
    "sns.violinplot(data=train, x='Outcome', y='SkinThickness', hue='Outcome')\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('SkinThickness')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三头肌皮褶厚度和是否发病也无法明显看出关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 餐后血清胰岛素\n",
    "sns.distplot(train['Insulin'], kde=False)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0为缺失数据，餐后血清胰岛素普遍集中在200以内，超过400较少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Insulin')"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 餐后血清胰岛素和得病关系\n",
    "sns.violinplot(data=train, x='Outcome', y='Insulin', hue='Outcome')\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Insulin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "明显可见，得病人群的餐后血清胰岛素相对未得病的较高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMI\n",
    "sns.distplot(train['BMI'], kde=False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0为缺失数据，BMI符合正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'BMI')"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMI和得病的关系\n",
    "sns.violinplot(data=train, x='Outcome', y='BMI', hue='Outcome')\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('BMI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得病人群的BMI相对未得病的较高一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 糖尿病家系作用\n",
    "sns.distplot(train['DiabetesPedigreeFunction'], kde=False)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'DiabetesPedigreeFunction')"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 糖尿病家系作用和得病的关系\n",
    "sns.violinplot(data=train, x='Outcome', y='DiabetesPedigreeFunction', hue='Outcome')\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('DiabetesPedigreeFunction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得病人群较未得病人群的糖尿病家系作用比例要多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄\n",
    "sns.distplot(train['Age'], kde=False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本的年龄段涵盖20-70，大部分是青年和中年"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Age')"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄和得病的关系\n",
    "sns.countplot(data=train, x='Age', hue='Outcome')\n",
    "plt.xlabel('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "年龄增大以后，得病率逐渐升高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(9.0, 0.0)"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 936x648 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征之间的关系\n",
    "data_corr = train.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "hp = sns.heatmap(data_corr,annot=True)\n",
    "# 调整高低\n",
    "bottom, top = hp.get_ylim()\n",
    "hp.set_ylim(bottom=bottom + 0.5, top=top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除目标外的特征之间的关系都比较弱，这有利于对目标的准确性,跟糖尿病关系比较强的有血浆葡萄糖浓度、饭后血清胰岛素浓度、BMI、糖尿病家系作用、还有年龄、怀孕次数"
   ]
  }
 ]
}